pandemic influenza is a real

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pandemic influenza is a real serious disease
Hien TT et al., New England J Med
20043501179-1188
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the modeling problem

• influenza pandemics in 20th Century
• 1918 1957 1968
• massive notable impacts
• experts predict another pandemic
• can not reliable predict when
• can not reliable predict how, who, where
• plan for next pandemic
• it is a global health priority
• need numbers

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PAHO/CDC pandemic impact assessment workshops

• to estimate the burden of disease attributable to
  influenza pandemic excess of deaths,
  hospitalizations, and outpatient visits
• to evaluate the response capacity (i.e., the
  potential to cope with excess demand) from
  hospitals and outpatient health facilities (surge
  capacity)
• to estimate the magnitude of the loss of workdays
• to derive gross estimates of direct economic
  impact attributable to pandemic influenza

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FluSoftware FluAid, FluSurge FluWorkLoss
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1957-58 influenza pandemic geographic spread
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FluSoft modeling data assumptions
default values US national estimates CDC's
Advisory Committee on Immunization Practices, ACIP
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FluSoft modeling data assumptions
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FluSoft modeling data assumptions
other assumptions employment marriage rate
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1918-19 influenza pandemic geographic spread
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FluSoft modeling data assumptions
Locales, all US 1 (New London Baltimore
Maryland) 2 (Macon Spartanburg San Antonio
Augusta Des Moines Little Rock Louisville) 3
(San Francisco)
Frost WH. Public Health Reports 192035584-97
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FluSoft modeling data assumptions
The scaling factor was obtained by comparing the
calculated death rates, by age group, from
estimates of death for the U.S. population
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estimated pandemic impact in the world
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estimated pandemic impact in the US
Source U.S. Dept Health and Human Services
Pandemic Influenza Plan Part 1. Page
18. Available at http//www.dhhs.gov/pandemicflu
/plan/pdf/part1.pdf
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estimated potential impact in LAC
clinical attack rate 25 first pandemic wave
(8 weeks) FluAid/FluSurge modeling Pandemic
Impact Subregional Workshops Nov/Dec 2005
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estimated potential impact in LAC
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Latin America the Caribbeandistribution of
hospital admission excess(AT25 scenario 1968
1st pandemic wave)
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Latin America the Caribbeanpotential pandemic
impact on health services(AT25 scenario 1968
1st pandemic wave)
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Latin America the Caribbeanworkdays loss
attributable to influenza pandemic
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Latin America the Caribbeancosts from
workdays lost due to pandemic influenza
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Latin America the Caribbeannon-discounted
value of human life lost due to influenza
pandemic (ppp)
direct cost from excess hospitalization (ICU
non-ICU) and excess outpatient visits has not yet
been summarized.
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on estimations

• they are PRELIMINARY, and EXPLORATORY
• they are ILLUSTRATIVE
• they are NOT PREDICTIONS of what inevitably may
  happen
• they should serve as an AID IN PLANNING

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what good are models?

• first, because life is full of choices, risks,
  uncertainty, and trade-offs
• then, because we do need to make sound decisions
  in a setting of absence of absolute certainty
  (and we do need a rationale for them)
• and then because sound decisions demand evidence,
  structure, consistency, and simplification
• models illustrate level of knowledge
• models show how we think things are connected and
  happen
• models add simplification helps clarify
• models can help identify what is most important
• sensitivity analyses, confidence intervals
• no single answer

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outbreak detection response
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timely outbreak detection response
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pandemic spread potential
What must happen at time 4 for this event to
continue be seen as a public health problem?
Basically, an equal number of incident (new)
cases must be generated in the population per
unit time
In other words, the goal for each new case at
time t is to infect one up to time t1
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to understand this, we need to build a model

• Why? because we need evidence, structure,
  consistency, and simplification in order to make
  sound decisions

At any given moment in time, the fraction of
susceptible persons in a population is dependent
upon (a function of) the number of susceptible
persons who die minus the number of susceptible
persons who become infective. The latter
fluctuates in a cyclical pattern.
N total population X number of
susceptible persons (as a fraction of N) Y
number of infectives µ death rate ?
transmission rate dß force of infection ?
angular frequency or oscillation in susceptibility
Duncan et al. The dynamics of measles epidemics.
Theoret Pop Bio 199752155-163
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pandemic spread potential
k .
ß .
D
1
R0
R0 basic reproductive rate of an epidemic
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pandemic influenza propagation dynamics
duration of infectivity (D, days)
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so, lets build a simple model

• influenza pandemic propagation
• assume a basic reproductive rate (Ro) 1.5
• assume an average generation time (days) 3.0

then repeat
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pandemic spread according to our simple model
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epidemic propagation scenarios
R0 basic reproductive rate of an epidemic
R0 gt 1 epidemic expansion
R0 1 epidemic equilibrium
R0 lt 1 epidemic contraction
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epidemiological goal for outbreak containment
it is imperative to quickly reduce R0 in situ
(locally)
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correlation of interventionsfor pandemic
containment
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pandemic influenza potencial for containment
AvT antiviral therapy Pv
pre-vaccination AvCp antiviral
chemoprophylaxis QI quarantine isolation
Longini IM et al Science 2005309
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pandemic containment evidence from modeling
Longini IM et al Science 2005309
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pandemic containment evidence from modeling
GTAP Geographically Targeted Antiviral
Profilaxis Longini IM et
al Science 2005309
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pandemic containment evidence from modeling
Longini IM et al Am J Epidemiol 2004159
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pandemic containment evidence from modeling
Longini IM et al Am J Epidemiol 2004159
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the most certain model
unprepared
impact
disease burden HS surge capacity economic social
prepared
time (weeks)
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as a way of conclusion
What to do?, Take home message?

• Plan, Plan, Plan
• Prepare, Prepare, Prepare
• Practice, Practice, Practice

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